我正在开发一个图像识别应用程序,有些东西让我烦恼。
我正在使用双色调图像(文档)。如果我制作相同图像文件的副本并比较两者(实际比较两个[相同子区域]的子区域),我不会使用SURF获得100%匹配。这是我的代码的一部分:
public double FindPercentMatch(Bitmap obj, Bitmap scene)
{
// get scene key points
VectorOfKeyPoint scenePoints = GetKeyPoints((Bitmap)scene.Clone());
// if no scene points found, no need to go any further
if (scenePoints == null || scenePoints.Size == 0) { return 0; }
// get object key points
VectorOfKeyPoint objectPoints = GetKeyPoints((Bitmap)obj.Clone());
// if not enough object key points found vs scene key points, then match can't be close enough (since
// you can't have more matches than scene points anyway, so don't even try to match
if (objectPoints == null || objectPoints.Size == 0 || (scenePoints.Size / objectPoints.Size < .15)) { return 0; }
// we have enough key points, so compute descriptors for scene and object
Matrix<float> objectDescriptors = GetDescriptors(objectPoints,(Bitmap)obj.Clone());
Matrix<float> sceneDescriptors = GetDescriptors(scenePoints, (Bitmap)scene.Clone());
int objectDescriptorCount = objectDescriptors == null ? 0 : objectDescriptors.Size.Height;
int sceneDescriptorCount = sceneDescriptors == null ? 0 : sceneDescriptors.Size.Height;
// find matches
int sim = FindMatches(objectDescriptors, sceneDescriptors);
// for testing so we know how many were found so we can monitor it
log.Debug("descriptors1 = " + objectDescriptorCount + ", 2 = " + sceneDescriptorCount + ", matches=" + sim);
double percent = 0;
if (sim != 0)
{
percent = objectDescriptorCount != 0 ? (double)sim / (double)objectDescriptorCount : 0;
log.Debug(percent * 100 + "%");
}
return percent;
}
public int FindMatches(Matrix<float> dbDescriptors, Matrix<float> queryDescriptors)
{
double uniquenessThreshold = 0.6;
if (dbDescriptors == null || queryDescriptors == null)
{
return 0;
}
var indices = new Matrix<int>(queryDescriptors.Rows, 2); // matrix that will contain indices of the 2-nearest neighbors found
var dists = new Matrix<float>(queryDescriptors.Rows, 2); // matrix that will contain distances to the 2-nearest neighbors found
// create FLANN index with 4 kd-trees and perform KNN search over it look for 2 nearest neighbours
var flannIndex = new Index(dbDescriptors, 4);
flannIndex.KnnSearch(queryDescriptors, indices, dists, 2, 24);
// for eatch match over a certain threshold, add +1 to the number of 'good' matches
int sim = 0;
for (int i = 0; i < indices.Rows; i++)
{
// filter out all inadequate pairs based on distance between pairs
if (dists.Data[i, 0] < (uniquenessThreshold * dists.Data[i, 1]))
{
sim++;
}
}
return sim;
}
在图像的同一子区域使用FindPercentMatch时的预期结果将是100%,但根据图像,它可能是70%,99%,我甚至看到了101%。
答案 0 :(得分:0)
据我所知,您的过滤不会导致1对1的objectdescriptor - scenedescriptor(匹配超过某个阈值),这就是为什么你可以得到100 +%
另一件事是&#34; objectDescriptorCount等于sceneDescriptorCount&#34;并不总是意味着&#34; objectDescriptors等于sceneDescriptors&#34;。手动检查描述符,如果它们相同则意味着您的处理逻辑出错了(计算&#34; sim&#34;)
希望有所帮助
答案 1 :(得分:0)
我认为我已经通过使用强力匹配器而不是flann索引修复了我的问题。此外,我没有使用2nn并进行比较,而是找到了第一个最近邻居,并将任何低于某个阈值的人计算为“良好匹配”。然后我将好匹配除以OBJECT(不是场景)的关键点总数,所以我现在最终得到0-100%的匹配。
int numberOfNearestNeighbors = 1;
double maxDistance = .30;
BruteForceMatcher<float> matcher = new BruteForceMatcher<float>(DistanceType.L2);
matcher.Add(dbDescriptors);
var indices = new Matrix<int>(queryDescriptors.Rows, numberOfNearestNeighbors);
using (Matrix<float> dist = new Matrix<float>(queryDescriptors.Rows, numberOfNearestNeighbors))
{
// null = no mask, use whole image
matcher.KnnMatch(queryDescriptors, indices, dist, numberOfNearestNeighbors, null);
int matchesUnderMaxDistance = 0;
float distance = 0;
// filter matches that are too different
for (int i = 0; i < indices.Rows; i++)
{
distance = dist.Data[i, 0];
if (distance < maxDistance)
{
matchesUnderMaxDistance++;
}
}
return matchesUnderMaxDistance;
}